advanced_humanizer_v2.py

import re
import random
import nltk
import numpy as np
from typing import List, Dict, Optional, Tuple
import time
import math
from collections import Counter, defaultdict
import statistics

# Download required NLTK data
def ensure_nltk_data():
    try:
        nltk.data.find('tokenizers/punkt')
    except LookupError:
        nltk.download('punkt', quiet=True)

    try:
        nltk.data.find('corpora/wordnet')
    except LookupError:
        nltk.download('wordnet', quiet=True)

    try:
        nltk.data.find('corpora/omw-1.4')
    except LookupError:
        nltk.download('omw-1.4', quiet=True)
        
    try:
        nltk.data.find('taggers/averaged_perceptron_tagger')
    except LookupError:
        nltk.download('averaged_perceptron_tagger', quiet=True)

ensure_nltk_data()

from nltk.tokenize import sent_tokenize, word_tokenize
from nltk import pos_tag
from nltk.corpus import wordnet

# Advanced imports with fallbacks
def safe_import_with_detailed_fallback(module_name, component=None, max_retries=2):
    """Import with fallbacks and detailed error reporting"""
    for attempt in range(max_retries):
        try:
            if component:
                module = __import__(module_name, fromlist=[component])
                return getattr(module, component), True
            else:
                return __import__(module_name), True
        except ImportError as e:
            if attempt == max_retries - 1:
                print(f"❌ Could not import {module_name}.{component if component else ''}: {e}")
                return None, False
        except Exception as e:
            print(f"❌ Error importing {module_name}: {e}")
            return None, False
    return None, False

# Advanced model imports
print("🧠 Loading Advanced AI Text Humanizer...")
SentenceTransformer, SENTENCE_TRANSFORMERS_AVAILABLE = safe_import_with_detailed_fallback('sentence_transformers', 'SentenceTransformer')
pipeline, TRANSFORMERS_AVAILABLE = safe_import_with_detailed_fallback('transformers', 'pipeline')

try:
    from sklearn.feature_extraction.text import TfidfVectorizer
    from sklearn.metrics.pairwise import cosine_similarity as sklearn_cosine_similarity
    SKLEARN_AVAILABLE = True
except ImportError:
    SKLEARN_AVAILABLE = False

try:
    import torch
    TORCH_AVAILABLE = True
except ImportError:
    TORCH_AVAILABLE = False

class AdvancedAITextHumanizer:
    """
    Advanced AI Text Humanizer based on research from QuillBot, ChatGPT, and BypassGPT
    Implements cutting-edge techniques to make AI text undetectable
    """
    
    def __init__(self, enable_gpu=True, aggressive_mode=False):
        print("🚀 Initializing Advanced AI Text Humanizer...")
        print("📊 Based on research from QuillBot, BypassGPT, and academic papers")
        
        self.enable_gpu = enable_gpu and TORCH_AVAILABLE
        self.aggressive_mode = aggressive_mode
        
        # Initialize advanced models
        self._load_advanced_models()
        self._initialize_humanization_database()
        self._setup_detection_evasion_patterns()
        
        print("✅ Advanced AI Text Humanizer ready!")
        self._print_capabilities()
    
    def _load_advanced_models(self):
        """Load advanced NLP models for humanization"""
        self.similarity_model = None
        self.paraphraser = None
        
        # Load sentence transformer for semantic analysis
        if SENTENCE_TRANSFORMERS_AVAILABLE:
            try:
                print("📥 Loading advanced similarity model...")
                device = 'cuda' if self.enable_gpu and TORCH_AVAILABLE and torch.cuda.is_available() else 'cpu'
                self.similarity_model = SentenceTransformer('all-MiniLM-L6-v2', device=device)
                print("✅ Advanced similarity model loaded")
            except Exception as e:
                print(f"⚠️ Could not load similarity model: {e}")
        
        # Load paraphrasing model
        if TRANSFORMERS_AVAILABLE:
            try:
                print("📥 Loading advanced paraphrasing model...")
                device = 0 if self.enable_gpu and TORCH_AVAILABLE and torch.cuda.is_available() else -1
                self.paraphraser = pipeline(
                    "text2text-generation",
                    model="google/flan-t5-base",  # Larger model for better quality
                    device=device,
                    max_length=512
                )
                print("✅ Advanced paraphrasing model loaded")
            except Exception as e:
                print(f"⚠️ Could not load paraphrasing model, trying smaller model: {e}")
                try:
                    self.paraphraser = pipeline(
                        "text2text-generation",
                        model="google/flan-t5-small",
                        device=device,
                        max_length=512
                    )
                    print("✅ Fallback paraphrasing model loaded")
                except Exception as e2:
                    print(f"⚠️ Could not load any paraphrasing model: {e2}")
        
        # Initialize fallback TF-IDF
        if SKLEARN_AVAILABLE:
            self.tfidf_vectorizer = TfidfVectorizer(
                stop_words='english', 
                ngram_range=(1, 3),
                max_features=10000
            )
        else:
            self.tfidf_vectorizer = None
    
    def _initialize_humanization_database(self):
        """Initialize comprehensive humanization patterns based on research"""
        
        # Extended formal-to-casual mappings (QuillBot style)
        self.formal_to_casual = {
            # Academic/business formal words
            "utilize": ["use", "employ", "apply"],
            "demonstrate": ["show", "prove", "reveal", "display"],
            "facilitate": ["help", "enable", "assist", "make easier"],
            "implement": ["do", "carry out", "execute", "put in place"],
            "consequently": ["so", "therefore", "as a result", "thus"],
            "furthermore": ["also", "plus", "additionally", "what's more"],
            "moreover": ["also", "besides", "furthermore", "on top of that"],
            "nevertheless": ["but", "however", "still", "yet"],
            "subsequently": ["then", "later", "after that", "next"],
            "accordingly": ["so", "therefore", "thus", "hence"],
            "regarding": ["about", "concerning", "on", "as for"],
            "pertaining": ["about", "related to", "concerning", "regarding"],
            "approximately": ["about", "around", "roughly", "nearly"],
            "endeavor": ["try", "attempt", "effort", "work"],
            "commence": ["start", "begin", "kick off", "get going"],
            "terminate": ["end", "stop", "finish", "conclude"],
            "obtain": ["get", "acquire", "receive", "secure"],
            "purchase": ["buy", "get", "acquire", "pick up"],
            "examine": ["look at", "check", "study", "review"],
            "analyze": ["study", "examine", "look into", "break down"],
            "construct": ["build", "make", "create", "put together"],
            "establish": ["set up", "create", "form", "start"],
            
            # Advanced academic terms
            "methodology": ["method", "approach", "way", "process"],
            "systematic": ["organized", "structured", "methodical", "orderly"],
            "comprehensive": ["complete", "thorough", "full", "extensive"],
            "significant": ["important", "major", "big", "notable"],
            "substantial": ["large", "considerable", "major", "significant"],
            "optimal": ["best", "ideal", "perfect", "top"],
            "sufficient": ["enough", "adequate", "plenty", "satisfactory"],
            "adequate": ["enough", "sufficient", "acceptable", "decent"],
            "exceptional": ["amazing", "outstanding", "remarkable", "extraordinary"],
            "predominant": ["main", "primary", "chief", "leading"],
            "fundamental": ["basic", "essential", "core", "key"],
            "essential": ["key", "vital", "crucial", "important"],
            "crucial": ["key", "vital", "essential", "critical"],
            "paramount": ["most important", "crucial", "vital", "key"],
            "imperative": ["essential", "crucial", "vital", "necessary"],
            "mandatory": ["required", "necessary", "compulsory", "obligatory"],
            
            # Technical jargon
            "optimization": ["improvement", "enhancement", "betterment", "upgrade"],
            "enhancement": ["improvement", "upgrade", "boost", "betterment"],
            "implementation": ["execution", "carrying out", "putting in place", "doing"],
            "utilization": ["use", "usage", "employment", "application"],
            "evaluation": ["assessment", "review", "analysis", "examination"],
            "assessment": ["evaluation", "review", "analysis", "check"],
            "validation": ["confirmation", "verification", "proof", "checking"],
            "verification": ["confirmation", "validation", "checking", "proof"],
            "consolidation": ["combining", "merging", "uniting", "bringing together"],
            "integration": ["combining", "merging", "blending", "bringing together"],
            "transformation": ["change", "conversion", "shift", "alteration"],
            "modification": ["change", "alteration", "adjustment", "tweak"],
            "alteration": ["change", "modification", "adjustment", "shift"]
        }
        
        # AI-specific phrase patterns (BypassGPT research)
        self.ai_phrases = {
            "it's important to note that": ["by the way", "worth mentioning", "interestingly", "note that"],
            "it should be emphasized that": ["importantly", "remember", "keep in mind", "crucially"],
            "it is worth mentioning that": ["by the way", "also", "incidentally", "note that"],
            "it is crucial to understand that": ["importantly", "remember", "you should know", "crucially"],
            "from a practical standpoint": ["practically speaking", "in practice", "realistically", "in real terms"],
            "from an analytical perspective": ["analytically", "looking at it closely", "from analysis", "examining it"],
            "in terms of implementation": ["when implementing", "for implementation", "practically", "in practice"],
            "with respect to the aforementioned": ["regarding what was mentioned", "about that", "concerning this", "as for that"],
            "as previously mentioned": ["as I said", "like I mentioned", "as noted before", "earlier I said"],
            "in light of this": ["because of this", "given this", "considering this", "with this in mind"],
            "it is imperative to understand": ["you must understand", "it's crucial to know", "importantly", "you need to know"],
            "one must consider": ["you should think about", "consider", "think about", "keep in mind"],
            "it is evident that": ["clearly", "obviously", "it's clear that", "you can see that"],
            "it can be observed that": ["you can see", "it's clear", "obviously", "evidently"],
            "upon careful consideration": ["thinking about it", "considering this", "looking at it closely", "after thinking"],
            "in the final analysis": ["ultimately", "in the end", "finally", "when all is said and done"]
        }
        
        # Advanced contraction patterns
        self.contractions = {
            "do not": "don't", "does not": "doesn't", "did not": "didn't",
            "will not": "won't", "would not": "wouldn't", "should not": "shouldn't",
            "could not": "couldn't", "cannot": "can't", "is not": "isn't",
            "are not": "aren't", "was not": "wasn't", "were not": "weren't",
            "have not": "haven't", "has not": "hasn't", "had not": "hadn't",
            "I am": "I'm", "you are": "you're", "he is": "he's", "she is": "she's",
            "it is": "it's", "we are": "we're", "they are": "they're",
            "I have": "I've", "you have": "you've", "we have": "we've",
            "they have": "they've", "I will": "I'll", "you will": "you'll",
            "he will": "he'll", "she will": "she'll", "it will": "it'll",
            "we will": "we'll", "they will": "they'll",
            "would have": "would've", "should have": "should've",
            "could have": "could've", "might have": "might've",
            "must have": "must've", "need not": "needn't",
            "ought not": "oughtn't", "dare not": "daren't"
        }
        
        # Human-like transition words
        self.human_transitions = [
            "Look,", "Listen,", "Here's the thing:", "You know what?",
            "Actually,", "Honestly,", "Frankly,", "To be honest,",
            "In my opinion,", "I think", "I believe", "It seems to me",
            "From what I can tell,", "As I see it,", "The way I look at it,",
            "Let me put it this way:", "Here's what I mean:", "In other words,",
            "What I'm saying is,", "The point is,", "Bottom line,",
            "At the end of the day,", "When it comes down to it,",
            "The truth is,", "Real talk,", "Between you and me,",
            "If you ask me,", "In my experience,", "From my perspective,"
        ]
        
        # Sentence starters that add personality
        self.personality_starters = [
            "You know,", "I mean,", "Well,", "So,", "Now,", "Look,",
            "Listen,", "Hey,", "Sure,", "Yeah,", "Okay,", "Right,",
            "Basically,", "Essentially,", "Obviously,", "Clearly,",
            "Apparently,", "Surprisingly,", "Interestingly,", "Funny thing is,"
        ]
        
        # Filler words and natural imperfections
        self.filler_words = [
            "like", "you know", "I mean", "sort of", "kind of",
            "basically", "actually", "literally", "really", "pretty much",
            "more or less", "somewhat", "rather", "quite", "fairly"
        ]
    
    def _setup_detection_evasion_patterns(self):
        """Setup patterns to evade AI detection based on research"""
        
        # Patterns that trigger AI detection (to avoid)
        self.ai_detection_triggers = {
            'repetitive_sentence_structure': r'^(The|This|It|That)\s+\w+\s+(is|are|was|were)\s+',
            'overuse_of_furthermore': r'\b(Furthermore|Moreover|Additionally|Subsequently|Consequently)\b',
            'perfect_grammar': r'^\s*[A-Z][^.!?]*[.!?]\s*$',
            'uniform_sentence_length': True,  # Check programmatically
            'lack_of_contractions': True,     # Check programmatically
            'overuse_of_passive_voice': r'\b(is|are|was|were|been|being)\s+\w+ed\b',
            'technical_jargon_clusters': True,  # Check programmatically
            'lack_of_personality': True        # Check programmatically
        }
        
        # Burstiness patterns (sentence length variation)
        self.burstiness_targets = {
            'short_sentence_ratio': 0.3,    # 30% short sentences (1-10 words)
            'medium_sentence_ratio': 0.5,   # 50% medium sentences (11-20 words)
            'long_sentence_ratio': 0.2      # 20% long sentences (21+ words)
        }
        
        # Perplexity enhancement techniques
        self.perplexity_enhancers = [
            'unexpected_word_choices',
            'colloquial_expressions',
            'regional_variations',
            'emotional_language',
            'metaphors_and_analogies'
        ]
    
    def calculate_perplexity(self, text: str) -> float:
        """Calculate text perplexity (predictability measure)"""
        words = word_tokenize(text.lower())
        if len(words) < 2:
            return 1.0
        
        # Simple n-gram based perplexity calculation
        word_counts = Counter(words)
        total_words = len(words)
        
        # Calculate probability of each word
        perplexity_sum = 0
        for i, word in enumerate(words[1:], 1):
            prev_word = words[i-1]
            # Probability based on frequency
            prob = word_counts[word] / total_words
            if prob > 0:
                perplexity_sum += -math.log2(prob)
        
        return perplexity_sum / len(words) if words else 1.0
    
    def calculate_burstiness(self, text: str) -> float:
        """Calculate text burstiness (sentence length variation)"""
        sentences = sent_tokenize(text)
        if len(sentences) < 2:
            return 0.0
        
        # Calculate sentence lengths
        lengths = [len(word_tokenize(sent)) for sent in sentences]
        
        # Calculate coefficient of variation (std dev / mean)
        mean_length = statistics.mean(lengths)
        if mean_length == 0:
            return 0.0
        
        std_dev = statistics.stdev(lengths) if len(lengths) > 1 else 0
        burstiness = std_dev / mean_length
        
        return burstiness
    
    def enhance_perplexity(self, text: str, intensity: float = 0.3) -> str:
        """Enhance text perplexity by adding unexpected elements"""
        sentences = sent_tokenize(text)
        enhanced_sentences = []
        
        for sentence in sentences:
            if random.random() < intensity:
                # Add unexpected elements
                words = word_tokenize(sentence)
                
                # Occasionally add filler words
                if len(words) > 5 and random.random() < 0.4:
                    insert_pos = random.randint(1, len(words)-1)
                    filler = random.choice(self.filler_words)
                    words.insert(insert_pos, filler)
                
                # Occasionally use unexpected synonyms
                if random.random() < 0.3:
                    for i, word in enumerate(words):
                        if word.lower() in self.formal_to_casual:
                            alternatives = self.formal_to_casual[word.lower()]
                            words[i] = random.choice(alternatives)
                
                sentence = ' '.join(words)
            
            enhanced_sentences.append(sentence)
        
        return ' '.join(enhanced_sentences)
    
    def enhance_burstiness(self, text: str, intensity: float = 0.7) -> str:
        """Enhance text burstiness by varying sentence structure"""
        sentences = sent_tokenize(text)
        enhanced_sentences = []
        
        for i, sentence in enumerate(sentences):
            words = word_tokenize(sentence)
            
            # Determine target sentence type based on position and randomness
            if random.random() < 0.3:  # Short sentence
                # Break long sentences or keep short ones
                if len(words) > 15:
                    # Find a natural break point
                    break_points = [j for j, word in enumerate(words) 
                                  if word.lower() in ['and', 'but', 'or', 'so', 'because', 'when', 'where', 'which']]
                    if break_points:
                        break_point = random.choice(break_points)
                        first_part = ' '.join(words[:break_point])
                        second_part = ' '.join(words[break_point+1:])
                        if second_part:
                            second_part = second_part[0].upper() + second_part[1:] if len(second_part) > 1 else second_part.upper()
                            enhanced_sentences.append(first_part + '.')
                            sentence = second_part
            
            elif random.random() < 0.2:  # Very short sentence for emphasis
                if len(words) > 8:
                    # Create a short, punchy version
                    key_words = [w for w in words if w.lower() not in ['the', 'a', 'an', 'is', 'are', 'was', 'were']][:4]
                    sentence = ' '.join(key_words) + '.'
            
            # Add personality starters occasionally
            if random.random() < intensity * 0.3:
                starter = random.choice(self.personality_starters)
                sentence = starter + ' ' + sentence.lower()
            
            enhanced_sentences.append(sentence)
        
        return ' '.join(enhanced_sentences)
    
    def apply_advanced_word_replacement(self, text: str, intensity: float = 0.8) -> str:
        """Apply advanced word replacement using multiple strategies"""
        words = word_tokenize(text)
        modified_words = []
        
        for i, word in enumerate(words):
            word_lower = word.lower().strip('.,!?;:"')
            replaced = False
            
            # Strategy 1: Direct formal-to-casual mapping
            if word_lower in self.formal_to_casual and random.random() < intensity:
                alternatives = self.formal_to_casual[word_lower]
                replacement = random.choice(alternatives)
                
                # Preserve case
                if word.isupper():
                    replacement = replacement.upper()
                elif word.istitle():
                    replacement = replacement.title()
                
                modified_words.append(replacement)
                replaced = True
            
            # Strategy 2: Contextual synonym replacement using WordNet
            elif not replaced and len(word) > 4 and random.random() < intensity * 0.4:
                try:
                    synsets = wordnet.synsets(word_lower)
                    if synsets:
                        # Get synonyms
                        synonyms = []
                        for syn in synsets[:2]:  # Check first 2 synsets
                            for lemma in syn.lemmas():
                                synonym = lemma.name().replace('_', ' ')
                                if synonym != word_lower and len(synonym) <= len(word) + 3:
                                    synonyms.append(synonym)
                        
                        if synonyms:
                            replacement = random.choice(synonyms)
                            if word.isupper():
                                replacement = replacement.upper()
                            elif word.istitle():
                                replacement = replacement.title()
                            modified_words.append(replacement)
                            replaced = True
                except:
                    pass
            
            if not replaced:
                modified_words.append(word)
        
        # Reconstruct text with proper spacing
        result = ""
        for i, word in enumerate(modified_words):
            if i > 0 and word not in ".,!?;:\"')":
                result += " "
            result += word
        
        return result
    
    def apply_advanced_contractions(self, text: str, intensity: float = 0.8) -> str:
        """Apply contractions with natural frequency"""
        # Sort contractions by length (longest first)
        sorted_contractions = sorted(self.contractions.items(), key=lambda x: len(x[0]), reverse=True)
        
        for formal, contracted in sorted_contractions:
            if random.random() < intensity:
                # Use word boundaries for accurate replacement
                pattern = r'\b' + re.escape(formal) + r'\b'
                text = re.sub(pattern, contracted, text, flags=re.IGNORECASE)
        
        return text
    
    def replace_ai_phrases(self, text: str, intensity: float = 0.9) -> str:
        """Replace AI-specific phrases with human alternatives"""
        for ai_phrase, alternatives in self.ai_phrases.items():
            if ai_phrase in text.lower():
                if random.random() < intensity:
                    replacement = random.choice(alternatives)
                    # Preserve case of first letter
                    if ai_phrase[0].isupper() or text.find(ai_phrase.title()) != -1:
                        replacement = replacement.capitalize()
                    
                    text = text.replace(ai_phrase, replacement)
                    text = text.replace(ai_phrase.title(), replacement.title())
                    text = text.replace(ai_phrase.upper(), replacement.upper())
        
        return text
    
    def add_natural_imperfections(self, text: str, intensity: float = 0.2) -> str:
        """Add subtle imperfections that humans naturally make"""
        sentences = sent_tokenize(text)
        imperfect_sentences = []
        
        for sentence in sentences:
            if random.random() < intensity:
                # Type of imperfection to add
                imperfection_type = random.choice([
                    'start_with_conjunction',
                    'end_without_period',
                    'add_hesitation',
                    'use_incomplete_thought'
                ])
                
                if imperfection_type == 'start_with_conjunction':
                    conjunctions = ['And', 'But', 'Or', 'So', 'Yet']
                    if not sentence.split()[0] in conjunctions:
                        sentence = random.choice(conjunctions) + ' ' + sentence.lower()
                
                elif imperfection_type == 'end_without_period':
                    if sentence.endswith('.'):
                        sentence = sentence[:-1]
                
                elif imperfection_type == 'add_hesitation':
                    hesitations = ['um,', 'uh,', 'well,', 'you know,']
                    words = sentence.split()
                    if len(words) > 3:
                        insert_pos = random.randint(1, len(words)-1)
                        words.insert(insert_pos, random.choice(hesitations))
                        sentence = ' '.join(words)
                
                elif imperfection_type == 'use_incomplete_thought':
                    if len(sentence.split()) > 10:
                        sentence = sentence + '... you know what I mean?'
            
            imperfect_sentences.append(sentence)
        
        return ' '.join(imperfect_sentences)
    
    def apply_advanced_paraphrasing(self, text: str, intensity: float = 0.4) -> str:
        """Apply advanced paraphrasing using transformer models"""
        if not self.paraphraser:
            return text
        
        sentences = sent_tokenize(text)
        paraphrased_sentences = []
        
        for sentence in sentences:
            if len(sentence.split()) > 8 and random.random() < intensity:
                try:
                    # Multiple paraphrasing strategies
                    strategies = [
                        f"Rewrite this naturally: {sentence}",
                        f"Make this more conversational: {sentence}",
                        f"Simplify this: {sentence}",
                        f"Rephrase casually: {sentence}",
                        f"Say this differently: {sentence}"
                    ]
                    
                    prompt = random.choice(strategies)
                    
                    result = self.paraphraser(
                        prompt,
                        max_length=min(200, len(sentence) + 50),
                        min_length=max(10, len(sentence) // 2),
                        num_return_sequences=1,
                        temperature=0.8,
                        do_sample=True
                    )
                    
                    paraphrased = result[0]['generated_text']
                    paraphrased = paraphrased.replace(prompt, '').strip().strip('"\'')
                    
                    # Quality checks
                    if (paraphrased and 
                        len(paraphrased) > 5 and 
                        len(paraphrased) < len(sentence) * 2.5 and
                        not paraphrased.lower().startswith(('i cannot', 'sorry', 'i can\'t'))):
                        
                        paraphrased_sentences.append(paraphrased)
                    else:
                        paraphrased_sentences.append(sentence)
                        
                except Exception as e:
                    print(f"⚠️ Paraphrasing failed: {e}")
                    paraphrased_sentences.append(sentence)
            else:
                paraphrased_sentences.append(sentence)
        
        return ' '.join(paraphrased_sentences)
    
    def calculate_advanced_similarity(self, text1: str, text2: str) -> float:
        """Calculate semantic similarity using advanced methods"""
        if self.similarity_model:
            try:
                embeddings1 = self.similarity_model.encode([text1])
                embeddings2 = self.similarity_model.encode([text2])
                similarity = np.dot(embeddings1[0], embeddings2[0]) / (
                    np.linalg.norm(embeddings1[0]) * np.linalg.norm(embeddings2[0])
                )
                return float(similarity)
            except Exception as e:
                print(f"⚠️ Advanced similarity failed: {e}")
        
        # Fallback to TF-IDF
        if self.tfidf_vectorizer and SKLEARN_AVAILABLE:
            try:
                tfidf_matrix = self.tfidf_vectorizer.fit_transform([text1, text2])
                similarity = sklearn_cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:2])[0][0]
                return float(similarity)
            except Exception as e:
                print(f"⚠️ TF-IDF similarity failed: {e}")
        
        # Basic word overlap similarity
        words1 = set(word_tokenize(text1.lower()))
        words2 = set(word_tokenize(text2.lower()))
        if not words1 or not words2:
            return 1.0 if text1 == text2 else 0.0
        
        intersection = words1.intersection(words2)
        union = words1.union(words2)
        return len(intersection) / len(union) if union else 1.0
    
    def humanize_text_advanced(self, 
                              text: str, 
                              style: str = "natural",
                              intensity: float = 0.8,
                              bypass_detection: bool = True,
                              preserve_meaning: bool = True,
                              quality_threshold: float = 0.7) -> Dict:
        """
        Advanced text humanization with cutting-edge techniques
        
        Args:
            text: Input text to humanize
            style: 'natural', 'casual', 'conversational', 'academic'
            intensity: Transformation intensity (0.0 to 1.0)
            bypass_detection: Enable AI detection bypass techniques
            preserve_meaning: Maintain semantic similarity
            quality_threshold: Minimum similarity to preserve
        """
        if not text.strip():
            return {
                "original_text": text,
                "humanized_text": text,
                "similarity_score": 1.0,
                "perplexity_score": 1.0,
                "burstiness_score": 0.0,
                "changes_made": [],
                "processing_time_ms": 0.0,
                "detection_evasion_score": 1.0,
                "quality_metrics": {}
            }
        
        start_time = time.time()
        original_text = text
        humanized_text = text
        changes_made = []
        
        # Calculate initial metrics
        initial_perplexity = self.calculate_perplexity(text)
        initial_burstiness = self.calculate_burstiness(text)
        
        # Phase 1: AI Detection Bypass (if enabled)
        if bypass_detection and intensity > 0.2:
            # Replace AI-specific phrases first
            before_ai_phrases = humanized_text
            humanized_text = self.replace_ai_phrases(humanized_text, intensity * 0.9)
            if humanized_text != before_ai_phrases:
                changes_made.append("Removed AI-specific phrases")
        
        # Phase 2: Advanced Word Replacement
        if intensity > 0.3:
            before_words = humanized_text
            humanized_text = self.apply_advanced_word_replacement(humanized_text, intensity * 0.8)
            if humanized_text != before_words:
                changes_made.append("Applied advanced word replacement")
        
        # Phase 3: Contraction Enhancement
        if intensity > 0.4:
            before_contractions = humanized_text
            humanized_text = self.apply_advanced_contractions(humanized_text, intensity * 0.7)
            if humanized_text != before_contractions:
                changes_made.append("Enhanced with natural contractions")
        
        # Phase 4: Perplexity Enhancement
        if intensity > 0.5:
            before_perplexity = humanized_text
            humanized_text = self.enhance_perplexity(humanized_text, intensity * 0.4)
            if humanized_text != before_perplexity:
                changes_made.append("Enhanced text perplexity")
        
        # Phase 5: Burstiness Enhancement
        if intensity > 0.6:
            before_burstiness = humanized_text
            humanized_text = self.enhance_burstiness(humanized_text, intensity * 0.6)
            if humanized_text != before_burstiness:
                changes_made.append("Enhanced sentence burstiness")
        
        # Phase 6: Advanced Paraphrasing
        if intensity > 0.7 and self.paraphraser:
            before_paraphrasing = humanized_text
            humanized_text = self.apply_advanced_paraphrasing(humanized_text, intensity * 0.3)
            if humanized_text != before_paraphrasing:
                changes_made.append("Applied AI-powered paraphrasing")
        
        # Phase 7: Natural Imperfections (for aggressive mode)
        if self.aggressive_mode and style in ["casual", "conversational"] and intensity > 0.8:
            before_imperfections = humanized_text
            humanized_text = self.add_natural_imperfections(humanized_text, intensity * 0.2)
            if humanized_text != before_imperfections:
                changes_made.append("Added natural imperfections")
        
        # Quality Control
        similarity_score = self.calculate_advanced_similarity(original_text, humanized_text)
        
        if preserve_meaning and similarity_score < quality_threshold:
            print(f"⚠️ Quality threshold not met (similarity: {similarity_score:.3f})")
            humanized_text = original_text
            similarity_score = 1.0
            changes_made = ["Quality threshold not met - reverted to original"]
        
        # Calculate final metrics
        final_perplexity = self.calculate_perplexity(humanized_text)
        final_burstiness = self.calculate_burstiness(humanized_text)
        processing_time = (time.time() - start_time) * 1000
        
        # Calculate detection evasion score
        detection_evasion_score = self._calculate_detection_evasion_score(
            original_text, humanized_text, changes_made
        )
        
        return {
            "original_text": original_text,
            "humanized_text": humanized_text,
            "similarity_score": similarity_score,
            "perplexity_score": final_perplexity,
            "burstiness_score": final_burstiness,
            "changes_made": changes_made,
            "processing_time_ms": processing_time,
            "detection_evasion_score": detection_evasion_score,
            "quality_metrics": {
                "perplexity_improvement": final_perplexity - initial_perplexity,
                "burstiness_improvement": final_burstiness - initial_burstiness,
                "word_count_change": len(humanized_text.split()) - len(original_text.split()),
                "character_count_change": len(humanized_text) - len(original_text),
                "sentence_count": len(sent_tokenize(humanized_text))
            }
        }
    
    def _calculate_detection_evasion_score(self, original: str, humanized: str, changes: List[str]) -> float:
        """Calculate how well the text evades AI detection"""
        score = 0.0
        
        # Score based on changes made
        if "Removed AI-specific phrases" in changes:
            score += 0.25
        if "Enhanced text perplexity" in changes:
            score += 0.20
        if "Enhanced sentence burstiness" in changes:
            score += 0.20
        if "Applied advanced word replacement" in changes:
            score += 0.15
        if "Enhanced with natural contractions" in changes:
            score += 0.10
        if "Applied AI-powered paraphrasing" in changes:
            score += 0.10
        
        # Bonus for variety
        if len(changes) > 3:
            score += 0.1
        
        return min(1.0, score)
    
    def _print_capabilities(self):
        """Print current capabilities"""
        print("\n📊 ADVANCED HUMANIZER CAPABILITIES:")
        print("-" * 45)
        print(f"🧠 Advanced Similarity: {'✅ ENABLED' if self.similarity_model else '❌ DISABLED'}")
        print(f"🤖 AI Paraphrasing: {'✅ ENABLED' if self.paraphraser else '❌ DISABLED'}")
        print(f"📊 TF-IDF Fallback: {'✅ ENABLED' if self.tfidf_vectorizer else '❌ DISABLED'}")
        print(f"🚀 GPU Acceleration: {'✅ ENABLED' if self.enable_gpu else '❌ DISABLED'}")
        print(f"⚡ Aggressive Mode: {'✅ ENABLED' if self.aggressive_mode else '❌ DISABLED'}")
        print(f"🎯 Detection Bypass: ✅ ENABLED")
        print(f"📝 Word Mappings: ✅ ENABLED ({len(self.formal_to_casual)} mappings)")
        print(f"🔤 AI Phrase Detection: ✅ ENABLED ({len(self.ai_phrases)} patterns)")
        print(f"📊 Perplexity Enhancement: ✅ ENABLED")
        print(f"📈 Burstiness Enhancement: ✅ ENABLED")
        
        # Calculate feature completeness
        total_features = 8
        enabled_features = sum([
            bool(self.similarity_model),
            bool(self.paraphraser),
            bool(self.tfidf_vectorizer),
            True,  # Word mappings
            True,  # AI phrase detection
            True,  # Perplexity enhancement
            True,  # Burstiness enhancement
            True   # Detection bypass
        ])
        
        completeness = (enabled_features / total_features) * 100
        print(f"🎯 Feature Completeness: {completeness:.1f}%")
        
        if completeness >= 90:
            print("🎉 ADVANCED HUMANIZER READY!")
        elif completeness >= 70:
            print("⚠️ Most features ready - some advanced capabilities limited")
        else:
            print("❌ Limited functionality - install additional dependencies")

# Convenience function for backward compatibility
def AITextHumanizer():
    """Factory function for backward compatibility"""
    return AdvancedAITextHumanizer()

# Test the advanced humanizer
if __name__ == "__main__":
    humanizer = AdvancedAITextHumanizer(aggressive_mode=True)
    
    test_cases = [
        {
            "text": "Furthermore, it is important to note that artificial intelligence systems demonstrate significant capabilities in natural language processing tasks. Subsequently, these systems can analyze and generate text with remarkable accuracy. Nevertheless, it is crucial to understand that human oversight remains essential for optimal performance.",
            "style": "conversational",
            "intensity": 0.9
        },
        {
            "text": "The implementation of comprehensive methodologies will facilitate optimization and enhance operational efficiency. Moreover, the utilization of systematic approaches demonstrates substantial improvements in performance metrics. Therefore, organizations should endeavor to establish frameworks that utilize these technologies effectively.",
            "style": "casual",
            "intensity": 0.8
        }
    ]
    
    print("\n🧪 TESTING ADVANCED HUMANIZER")
    print("=" * 40)
    
    for i, test_case in enumerate(test_cases, 1):
        print(f"\n🔬 Test {i}: {test_case['style'].title()} style")
        print("-" * 50)
        print(f"📝 Original: {test_case['text'][:100]}...")
        
        result = humanizer.humanize_text_advanced(**test_case)
        
        print(f"✨ Humanized: {result['humanized_text'][:100]}...")
        print(f"📊 Similarity: {result['similarity_score']:.3f}")
        print(f"🎯 Perplexity: {result['perplexity_score']:.3f}")
        print(f"📈 Burstiness: {result['burstiness_score']:.3f}")
        print(f"🛡️ Detection Evasion: {result['detection_evasion_score']:.3f}")
        print(f"⚡ Processing: {result['processing_time_ms']:.1f}ms")
        print(f"🔧 Changes: {', '.join(result['changes_made'])}")
    
    print(f"\n🎉 Advanced testing completed!")
    print(f"🚀 This humanizer uses cutting-edge techniques from QuillBot, BypassGPT research!")